• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.187-192
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• 2009

In this paper the extended algorithm of valley energy was proposed In order to detect the robust features of the face The proposed energy had the variable type without the constant size of valley mask for measuring the gray value among pixels By accumulating the results generated by the various masks the information of valley energy was so diversified. Then the robust energy which is independent of the environments was maded. The various experiments proved that The proposed method showed the detection rate of 98 percentage in the features of the face region.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.12
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• pp.71-83
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• 1996

This paper describes facial component detection and skew correction algorithm for face recognition. We use a priori knowledge and models about isolated regions to detect eye location from the face image captured in natural office environments. The relations between human face components are represented by several rules. We adopt an edge detection algorithm using sobel mask and 8-connected labelling algorith using array pointers. A labeled image has many isolated components. initially, the eye size rules are used. Eye size rules are not affected much by irregular input image conditions. Eye size rules size, and limited in the ratio between gorizontal and vertical sizes. By the eye size rule, 2 ~ 16 candidate eye components can be detected. Next, candidate eye parirs are verified by the information of location and shape, and one eye pair location is decided using face models about eye and eyebrow. Once we extract eye regions, we connect the center points of the two eyes and calculate the angle between them. Then we rotate the face to compensate for the angle so that the two eyes on a horizontal line. We tested 120 input images form 40 people, and achieved 91.7% success rate using eye size rules and face model. The main reasons of the 8.3% failure are due to components adjacent to eyes such as eyebrows. To detect facial components from the failed images, we are developing a mouth region processing module.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.413-414
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• 2024

영상 인식을 위한 학습 데이터 구성 단계에서 에지는 물체의 크기, 방향 등의 정보를 포함하고 있어 영상의 특징으로 사용한다. 본 논문에서는 얼굴 인식을 위하여 소벨 마스크를 사용하여 원영상과 압축영상 그리고 에지영상간의 학습에 따른 인식 정도를 파악하고자 한다. 실험결과, 원영상 그대로 인식하는 것보다 에지 영상에 의한 학습 속도에 차이가 있음을 알 수 있었다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.121-126
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• 2004

An efficient multi face detection technique is proposed based on hierarchical context-free attention operators in which multiple faces are efficiently detected from a noisy and complex background. A noise-tolerant generalized symmetry transform (NTSGT) is applied hierarchically, as a context free attention operator, to the input pyramidal image for the high speed global location of the regions of face candidates (ROFCs) with a single mask. For the face verification, local NTGST is applied within each ROFC to confirm the existence of the detailed facial features. First, by globally applying NTGST which introduces the average pyramid method and focusing to the input image with complex background, ROFCs with recognizable resolution are detected robustly. Morphological operations are applied only to the each detected ROFCs to emphasize the facial features like eyes and lips. Then, eyes are detected by locally appling NTGST to the ROFCs and only faces are detected by verifying the existence of the geometrical features of the faces relatively to the location of eyes. The experimental results show that the proposed method can efficiently detect multiple faces from a noisy or complex background with 93.5% detection rate.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.195-198
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• 2021

COVID-19 전염병은 우리의 일상 생활에 빠르게, 그리고 엄청난 영향을 미쳤다. 현재는 마스크를 착용하는 것이 새로운 평범함이 되었고, 이에 따라 많은 서비스 제공업체들은 고객들에게 그들의 서비스를 이용하기 위해 마스크를 착용하도록 요구하고 있다. 공공 버스도 이에 포함된다. 여러 뉴스 기사에 따르면 마스크를 써 달라는 버스 기사의 부탁에 버스 기사를 폭행한 사건이 여러 번 발생하였다. 이에 기계가 마스크를 쓰지 않은 사람을 가려내고 마스크를 쓰라고 한다면 버스 기사에게 향하는 비이성적 분노가 줄어들 것이라고 생각하였다. 따라서, 본 논문에서는 Keras와 같은 기본적인 기계 학습 패키지를 사용하여 빠르고 정확하게 마스크의 착용여부를 확인할 수 있는 방식을 제안한다. 제안된 방식은 고성능 컴퓨터 및 그래픽카드의 필요없이 CPU에서만 작동하는 마스크 착용 판별프로그렘으로, 추가적으로 알림을 보낼 수 있는 웹사이트와 음성 경고 시스템도 함께 구현하였다. 이 방법은 테스트 데이터셋에서 99.5% 이상의 정확도를 달성했고, GPU가 아닌 CPU에서 6fps 정도의 속도를 지원하여 실생활에 사용될 수 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1383-1390
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• 2012

In this paper, we propose a robust pupil detection method using rank order filter and pixel value difference in facial image. We have detected the potential pupil candidates using rank order filter. Many false pupil candidates found at eyebrow are removed using the fact that the pixel difference is much at the boundary between pupil and sclera. The rest pupil candidates are grouped into pairs. Each pair is verified according to geometric constraints such as the angle and the distance between two candidates. A fitness function is obtained for each pair using the pixel values of two pupil regions, we select a pair with the smallest fitness value as a final pupil. The experiments have been performed for 400 images of the BioID face database. The results show that it achieves more than 90% accuracy, and especially the proposed method improves the detection rate and high accuracy for face with spectacle.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.1-6
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• 2024

Recently, artificial neural networks have been playing a crucial role and advancing across various fields. Artificial neural networks are typically categorized into feedforward neural networks and recurrent neural networks. However, feedforward neural networks are primarily used for processing static spatial patterns such as image recognition and object detection. They are not suitable for handling temporal signals. Recurrent neural networks, on the other hand, face the challenges of complex training procedures and requiring significant computational power. In this paper, we propose memristors suitable for an advanced form of recurrent neural networks called reservoir computing systems, utilizing a mask processor. Using the characteristic equations of Ti/TiOx/TaOy/Pt, Pt/TiOx/Pt, and Ag/ZnO-NW/Pt memristors, we generated current-voltage curves to verify their memristive behavior through the confirmation of hysteresis. Subsequently, we trained and inferred reservoir computing systems using these memristors with the NIST TI-46 database. Among these systems, the accuracy of the reservoir computing system based on Ti/TiOx/TaOy/Pt memristors reached 99%, confirming the Ti/TiOx/TaOy/Pt memristor structure's suitability for inferring speech recognition tasks.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1855_1856
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• 2009

본 논문은 Haar-like 마스크와 유사한 특징을 갖지만 두 사각형 영역의 크기와 위치를 제한하지 않는 분리된 두 사각 특징 마스크를 이용한 Adaboost 기반 얼굴검출 알고리즘을 제안한다. 기존의 Haar-like 특징이 단순히 두 사각 영역의 화소값들의 차를 구함으로써 계산이 용이하나 인접한 두 사각 영역으로 한정함으로써 고품질 특징을 얻기 어렵다. 이런 Haar-like 특징마스크의 내재된 문제점을 개선하기 위해, 제안하는 특징 마스크는 다양한 크기와 분리된 두 사각 영역을 갖는 형태로 고품질의 특징을 얻는다. 고품질의 특징은 Adaboost 알고리즘의 약 분류기(weak classifier)의 성능을 학습단계부터 높여 전반적으로 얼굴 검출 알고리즘의 성능을 향상시킨다. 제안하는 분리된 두 사각 특징을 이용한 adaboost 기반 얼굴검출 기법의 우수성을 다양한 실험을 통해 검증하였다.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1657-1662
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• 2017

Facial expression recognition systems using video devices have emerged as an important component of natural human-machine interfaces which contribute to various practical applications such as security systems, behavioral science and clinical practices. In this work, we present a new method to analyze, represent and recognize human facial expressions using a sequence of facial images. Under our proposed facial expression recognition framework, the overall procedure includes: accurate face detection to remove background and noise effects from the raw image sequences and align each image using vertex mask generation. Furthermore, these features are reduced by principal component analysis. Finally, these augmented features are trained and tested using Hidden Markov Model (HMM). The experimental evaluation demonstrated the proposed approach over two public datasets such as Cohn-Kanade and AT&T datasets of facial expression videos that achieved expression recognition results as 96.75% and 96.92%. Besides, the recognition results show the superiority of the proposed approach over the state of the art methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.208-211
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• 2021

본 논문에서는 합성곱 신경망을 활용하여 영상에서 마스크 착용 및 미착용 상태를 탐지하는 방법을 제안한다. 코로나바이러스감염증-19(COVID-19)의 유행에 따라 감염 및 확산방지를 위해 마스크 정상적 착용이 요구되는데 몇몇 사람들은 이를 지키지 않고 있으며 현재의 감시 시스템은 입구에서 마스크 착용 여부를 검사하는 방식으로 작동될 뿐 공간에 입장한 다음 착용 여부를 알 수 없다. 제안하는 방법은 합성곱 신경망을 통해 영상에서 얼굴을 탐지하여 얻은 데이터를 이용하여 다수사람들의 마스크 착용 및 미착용 상태를 판별하는 방법으로 설계하였다.